Bat Coronavirus Phylogeography in the Western Indian Ocean

1 Bat coronavirus phylogeography in the Western Indian Ocean

2 Léa Joffrin a#, Steven M. Goodman b, c, David A. Wilkinson a, Beza Ramasindrazana a, b*, Erwan

3 Lagadec a, Yann Gomard a, Gildas Le Minter a, Andréa Dos Santos d, M. Corrie Schoeman e,

4 Rajendraprasad Sookhareea f, Pablo Tortosa a, Simon Julienne g, Eduardo S. Gudo h, Patrick

5 Mavingui a and Camille Lebarbenchon a#

6 a Université de La Réunion, UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT),

7 INSERM 1187, CNRS 9192, IRD 249, Sainte-Clotilde, La Réunion, France.

8 b Association Vahatra, Antananarivo, Madagascar.

9 c Field Museum of Natural History, Chicago, USA.

10 d Veterinary Faculty, Eduardo Mondlane University, Maputo, Mozambique.

11 e School of Life Sciences, University of Kwa-Zulu Natal, Kwa-Zulu Natal, South Africa.

12 f National Parks and Conservation Service, Réduit, Mauritius.

13 g Seychelles Ministry of Health, Victoria, Mahe, Seychelles.

14 h Instituto Nacional de Saúde, Maputo, Mozambique.

16 Running head: Bat coronavirus in the Western Indian Ocean

18 #Corresponding authors: [email protected]; [email protected]

19 UMR PIMIT, 2 rue Maxime Rivière, 97490 Sainte-Clotilde, Reunion Island, France.

21 *Present address: Beza Ramasindrazana, Institut Pasteur de Madagascar, BP 1274, Amba-

22 tofotsikely, Antananarivo 101, Madagascar

24 Abstract word count: 171 Manuscript word count: 3158

25 Author Contributions: CL and LJ conceived and designed the study. BR, CL, DAW, EL, LJ,

26 PT, SMG and YG collected biological material on Madagascar. SMG, RS and YG collected

27 biological material on Mauritius. BR, EL and GLM, collected biological material on Mayotte.

28 SMG, GLM, ADS and MCS, collected biological material in Mozambique. BR, DAW, EL and

29 GLM collected biological material on Reunion Island. EL, GLM, SJ and YG collected biolog-

30 ical material in the Seychelles. LJ performed the molecular analyses. CL, DAW, and LJ ana-

31 lyzed the data. CL and LJ wrote the paper. SMG, ESG, PM and SJ contributed to the project

32 management in Malagasy, Mauritian, Seychelles, Mozambican, and French institutions. All au-

33 thors edited, read, and approved the final manuscript.

35 Abstract

36 Bats provide key ecosystem services such as crop pest regulation, pollination, seed dispersal,

37 and soil fertilization. Bats are also major hosts for biological agents responsible for zoonoses,

38 such as coronaviruses (CoVs). The islands of the Western Indian Ocean are identified as a major

39 biodiversity hotspot, with more than 50 bat species. In this study, we tested 1,013 bats belonging

40 to 36 species from Mozambique, Madagascar, Mauritius, Mayotte, Reunion Island and Sey-

41 chelles, based on molecular screening and partial sequencing of the RNA-dependent RNA pol-

42 ymerase gene. In total, 88 bats (8.7%) tested positive for coronaviruses, with higher prevalence

43 in Mozambican bats (20.5% ± 4.9%) as compared to those sampled on islands (4.5% ± 1.5%).

44 Phylogenetic analyses revealed a large diversity of α- and β-CoVs and a strong signal of co-

45 evolution between CoVs and their bat host species, with limited evidence for host-switching,

46 except for bat species sharing day roost sites.

48 Importance

49 This is the first study to report the presence of coronaviruses (CoVs) in bats in Mayotte,

50 Mozambique and Reunion Island, and in insectivorous bats in Madagascar. Eight percent of the

51 tested bats were positive for CoVs, with higher prevalence in continental Africa than on islands.

52 A high genetic diversity of α- and β-CoVs was found, with strong association between bat host

53 and virus phylogenies, supporting a long history of co-evolution between bats and their associ-

54 ated CoVs in the Western Indian Ocean. These results highlight that strong variation between

55 islands does exist and is associated with the composition of the bat species community on each

56 island. Future studies should investigate whether CoVs detected in these bats have a potential

57 for spillover in other hosts.

61 Keywords: bat, coronavirus, islands, tropical, evolution, ecology

62 Text – 3158 words

63 Introduction

64 The burden of emerging infectious diseases has significantly increased over the last decades

65 and is recognized as a major global health concern. In 2018, the World Health Organization

66 (WHO) established the “Blueprint priority disease list”, identifying viruses such as Ebola, Lassa

67 fever, Middle East Respiratory Syndrome (MERS), and Nipah fever as significant threats to

68 international biosecurity 1. This list also highlights the potential pandemic risk from the emer-

69 gence of currently unknown zoonotic pathogens, collectively referring to these unknown threats

70 as “disease X” 1. Investigation of the potential zoonotic pathogens in wild animals, particularly

71 vertebrates, is thus critical for emerging infectious disease preparedness and responses.

72 Bats represent nearly 1,400 species and live on all continents except Antarctica 2. They

73 provide key ecosystem services such as crop pest regulation, pollination, seed dispersal, and

74 soil fertilization 3–10. Bats are also recognized as reservoirs of many zoonotic pathogens, includ-

75 ing coronaviruses (CoVs) 11–13. Indeed, several CoVs originating from bats have emerged in

76 humans and livestock with sometimes major impacts to public health. For instance, in 2003, the

77 Severe Acute Respiratory Syndrome (SARS) CoV emerged in humans, after spillover from bats

78 to civets14–18, and led to the infection of 8,096 people and 774 deaths in less than a year 19.

79 Our study area spans geographic locations across the islands of the Western Indian

80 Ocean and southeastern continental Africa (SECA) (Figure 1). These islands have diverse ge-

81 ological origins that have influenced the process of bat colonization and species distributions

82 20. The ecological settings and species diversity on these islands for bats are notably different.

83 On Madagascar, more than 45 bat species are known to occur, of which more than 80 % are

84 endemic to the island 21–23. The smaller studied islands of the Western Indian Ocean, Mauritius,

85 Mayotte, Reunion Island, and Mahé (Seychelles), host reduced bat species diversity (e.g. three

86 species on Reunion Island), whereas SECA supports a wide range of bat species. To date, sev-

87 eral studies have identified bat-infecting CoVs in countries of continental Africa, including

88 Zimbabwe 24, South Africa 25,26, Namibia 27, and Kenya 28,29. CoVs have also been reported in

89 fruit bats (Pteropodidae) in Madagascar, where β-coronaviruses belonging to the D-subgroup

90 were identified in Eidolon dupreanum and Pteropus rufus 30.

91 In this study, we investigated the presence of CoVs in over 1,000 individual bats be-

92 longing to 36 species, sampled on five islands (Madagascar, Mauritius, Mayotte, Reunion Is-

93 land, and Mahé) and one continental area (Mozambique). Based on molecular screening and

94 partial sequencing of the RNA-dependent RNA polymerase gene, we (i) estimated CoV preva-

95 lence in the regional bat populations, (ii) assessed CoV genetic diversity, and (iii) identified

96 associations between bat families and CoVs, as well as potential evolutionary drivers leading

97 to these associations.

99 Results

100 Prevalence of CoV

101 A total of 1,013 bats were tested from Mozambique, Mayotte, Reunion Island, Sey-

102 chelles, Mauritius and Madagascar (Figure 1). In total, 88 of the 1,013 bat samples tested pos-

103 itive for CoV by Real-Time PCR (mean detection rate: 8.7%). The prevalence of positive bats

104 was different according to the sampling locations (χ² = 77.0, df = 5; p<0.001), with a higher

105 prevalence in Mozambique (± 95% confidence interval: 20.5% ± 4.9%) than on all Western

106 Indian Ocean islands (4.5% ± 1.5%) (Figure 2). A significant difference in the prevalence of

107 positive bats was also detected between families (χ² = 44.8, df = 8; p<0.001; Supplementary

108 Figure S1). The highest prevalence were observed in the families Nycteridae (28.6 % ± 23.6%)

109 and Rhinolophidae (26.2% ± 11.0%). Bat species had a significant effect on the probability of

110 CoVs detection (χ² = 147.9, df = 39; p<0.001; Supplementary Figure S2). Finally, the preva-

111 lence of CoV positive bats in Mozambique was significantly different (N = 264, χ²= 22.8, df =

112 1; p<0.001; Supplementary Figure S3) between February (37.4% ± 9.9%) and May (11.6% ±

113 4.8).

114

115 RdRp sequence diversity

116 Of the 88 positive samples, we obtained 77 partial RdRp sequences using the Real-Time

117 PCR detection system (179 bp) and 51 longer partial RdRp sequences using a second PCR

118 system (440 bp). Sequences generated with the second system were subsequently used for phy-

119 logenetic analyses. Details of the sequenced CoV-positive samples are provided in Supplemen-

120 tary Table S1. Pairwise comparison of these 51 sequences revealed 28 unique sequences, and

121 sequences similarities ranging from 60.2% to 99.8%. The lowest sequence similarity was found

122 in Mozambique (60.2% to 99.8%), then in Madagascar (64.0% to 99.8%). No genetic variation

123 was observed for samples from Mayotte and Reunion Island.

124

125 Phylogenetic structure of CoVs

126 Sequence comparison indicated that Western Indian Ocean bats harbor a high diversity

127 of both α and β-CoVs, with conserved groups clustering mostly by bat family (Figure 3). Spe-

128 cifically, 25 sequences were identified as α-CoVs, and three sequences were genetically related

129 to the β-CoVs. For α-CoVs, all sequences detected in our tested Molossidae formed a highly

130 supported monophyletic group, including CoV sequences from Molossidae bats previously de-

131 tected in continental Africa (Figure 4). CoVs detected in Mops condylurus (Mozambique), Mor-

132 mopterus francoismoutoui (Reunion Island), Chaerephon pusillus and Chaerephon sp. (Ma-

133 yotte), and Mormopterus jugularis (Madagascar) shared 90% - 98% nucleotide similarity with

134 a CoV detected in Chaerephon sp. in Kenya (Supplementary Table S2). All CoVs found in

135 Miniopteridae clustered in a monophyletic group, including Miniopteridae CoVs sequences

136 from Africa, Asia, and Oceania (Supplementary Table S2). The great majority of α-CoVs de-

137 tected in Rhinolophidae bats clustered in two monophyletic groups (Figure 3); one with African

138 Rhinolophidae CoVs and one with Asian Rhinolophidae CoVs. We also detected one CoV from

139 Rhinolophus rhodesiae, which was 100% similar to a Miniopteridae CoV from this study. Rhi-

140 nonycteridae CoVs formed a single monophyletic group with NL63 Human CoVs. The Rhi-

141 nonycteridae CoVs detected clustered with NL63-related bat sequences found in Triaenops afer

142 in Kenya (Figure 5) and showed 85% similarity to NL63 Human CoVs (Supplementary Table

143 S2). Hipposideridae α-CoVs mainly clustered into a single monophyletic group, including 229E

144 Human CoV-related bat sequence found in Hipposideros vittatus from Kenya (Figure 6; Sup-

145 plementary Table S2).

146 For β-CoVs, two sequences obtained from Nycteris thebaica clustered in the C-sub-

147 group together with other CoVs previously reported in African Nycteris sp. bats (Figure 7). The

148 sequences showed 88% nucleotide identity to a β-C CoV found in Nycteris gambiensis in Ghana

149 (Supplementary Table S2). Rousettus madagascariensis CoVs clustered with Pteropodidae

150 CoVs belonging to the D-subgroup of β-CoVs (Figure 8). BLAST queries against the NCBI

151 database showed 98% nucleotide identity between CoV sequences from Rousettus madagasca-

152 riensis and a β-D CoV sequence detected in Eidolon helvum from Kenya (Supplementary Table

153 S2).

154

155 Co-phylogeny between bats and CoVs

156 Co-phylogeny tests were conducted using 11 Cyt b sequences obtained from the 11

157 CoVs positive bat species and 27 partial CoV RdRp sequences (440 bp). Results supported co-

158 evolution between the Western Indian Ocean bats and their CoVs (ParaFitGlobal = 0.04; p =

159 0.001) and a high level of phylogenetic congruence (Figure 9).

160

161 Discussion

162 We provide evidence for a high diversity of CoVs in bats on Western Indian Ocean islands.

163 The overall prevalence of CoV positive bats was consistent with studies from continental Africa

164 25 and from islands in the Australasian region 31, although we detected significant variation in

165 the prevalence of infected bats, according to their family, species, sampling location and season.

166 Our study is nevertheless affected by the strong heterogeneity of bat communities in the island

167 of the Western Indian Ocean, in particular in term of species richness. The high CoV genetic

168 diversity detected in bats from Mozambique and Madagascar is likely to be associated with the

169 higher bat species diversity in the African mainland and in Madagascar, has compared to small

170 oceanic islands 20. In addition, CoV prevalence in bat populations may significantly vary across

171 seasons, as found in Mozambique with higher prevalence during the wet season than in the dry

172 season. Several studies on bat CoV have indeed shown significant variations in the temporal

173 infection dynamic of CoV in bats, potentially associated with bat parturition 32–34.

174 Host specificity is well known for some bat CoVs subgenera 35–37. For example, β-C CoVs

175 are largely associated with Vespertilionidae, whereas β-D CoVs are found mostly in Pteropodi-

176 dae 36,38. In our study, we showed that Western Indian Ocean bats harbor phylogenetically struc-

177 tured CoVs, of both α-CoV and β-CoV subclades, clustering mostly by bat family. In the new

178 CoV taxonomy based on full genomes proposed by the International Committee of Taxonomy

179 of Viruses (ICTV), α-CoVs and β-CoVs are split in subgenera mostly based on host families 39,

180 reflected in the subgenera names (e.g. Rhinacovirus for a Rhinolophidae α-CoV cluster, Min-

181 uacovirus for a Miniopteridae α-CoV cluster, Hibecovirus for an Hipposideridae β-CoV clus-

182 ter). Although our classification was based on a partial sequence of the RdRp region, we iden-

183 tified sequences from samples belonging to four of these subgenera (Minuacovirus, Duvina-

184 covirus, Rhinacovirus, and Nobecovirus) and three that could not be classified according to this

185 taxonomic scheme hence representing unclassified subgenera (we propose “Molacovirus”,

186 “Nycbecovirus”, and “Rhinacovirus2”).

187 A strong geographical influence on CoVs diversity, with independent evolution of CoVs on

188 each island, was expected in our study, because of spatial isolation and endemism of the tested

189 bat species. Anthony et al. 38 found that the dominant evolutionary mechanism for African CoVs

190 was host switching. Congruence between host and viral phylogenies however suggests a strong

191 signal for co-evolution between Western Indian Ocean bats and their associated CoVs. Geo-

192 graphical influence seems to occur within bat families, as for Molossidae. Endemism resulting

193 from geographic isolation may thus have played a role in viral diversification within bat fami-

194 lies.

195 Although co-evolution could be the dominant mechanism in the Western Indian Ocean,

196 host-switching may take place in certain situations. For example, in Mozambique, we found a

197 potential Miniopteridae α-CoV in a Rhinolophidae bat co-roosting with Miniopteridae in the

198 same cave. These host-switching events could be favored when several bat species roost in

199 syntopy 40. A similar scenario was described in Australia where Miniopteridae α-CoV was de-

200 tected in Rhinolophidae bats 31. These infrequent host-switching events show that spillovers

201 can happen but suggest that viral transmission is not maintained in the receiver host species.

202 The host-virus co-evolution might thus have resulted in strong adaptation of CoVs to each bat

203 host species. In addition, viral factors (mutation rate, recombination propensity, replication abil-

204 ity in the cytoplasm, changes in the ability to bind host cells), environmental factors (climate

205 variation, habitat degradation, decrease of bat preys), and phylogenetic relatedness of host spe-

206 cies are also critical for the viral establishment in a novel host 41–44. Nevertheless, apparent

207 evidence of host switching as a dominant mechanism of CoV evolution could be an artifact of

208 a lack of data for some potential bat hosts, leading to incomplete phylogenetic reconstructions

209 38.

210 Several bat CoVs we identified in Rhinonycteridae and Hipposideridae from Mozambique

211 had between 85% and 93% nucleotide sequence similarity with NL63 Human CoVs and 229E

212 Human CoVs, respectively. These two human viruses are widely distributed in the world and

213 associated with mild to moderate respiratory infection in humans 45. Tao et al. established that

214 the NL63 Human CoVs and 229E Human CoVs have a zoonotic recombinant origin from their

215 most recent common ancestor, estimated to be about 1,000 years ago 46. During the past decade,

216 they were both detected in bats in Kenya, and in Ghana, Gabon, Kenya, and Zimbabwe, respec-

217 tively 24,28,47,48. Intermediate hosts are important in the spillover of CoVs, despite major

218 knowledge gaps on the transmission routes of bat infectious agents to secondary hosts 49. This

219 hypothesis has been formulated for the 229E Human CoV, with an evolutionary origin in Hip-

220 posideridae bats and with camelids as intermediate hosts 48. The ancient spillover of NL63 from

221 Rhinonycteridae bats to humans might have occurred through a currently unidentified interme-

222 diate host 28,50,51. Because receptor recognition by viruses is the first essential cellular step to

223 infect host cells, CoVs may have spilt over into humans from bats through an intermediate host

224 possibly due to mutations on spike genes 13,28. Further investigations of CoVs in Kenyan and

225 Mozambican livestock and hunted animals could potentially provide information on the com-

226 plete evolutionary and emergence history of these two viruses before their establishment in

227 humans.

228 MERS-like CoV, with high sequence similarity (>85%) to human and camel strains of

229 MERS-CoV, have been detected in Neoromicia capensis in South Africa and Pipistrellus cf.

230 hesperidus in Uganda, suggesting a possible origin of camel MERS-CoV in vespertilionid bats

231 25,38,52. This family has been widely studied, with 30% of all reported bat CoVs sequences from

232 the past 20 years coming from vespertilionids 53. No members of this family were positive for

233 CoV in our study, which may be associated with the low number of individuals tested; addi-

234 tional material is needed to explore potential MERS-like CoV in the Western Indian Ocean, in

235 particular on Madagascar.

236 Knowledge on bat CoV ecology and epidemiology has significantly increased during the

237 past decade. Anthony et al. estimated that there might be at least 3,204 bat CoVs worldwide 38;

238 however, direct bat-to-human transmission has not been demonstrated so far. As for most

239 emerging zoonoses, CoV spillover and emergence may be associated to human activities and

240 ecosystem changes such as habitat fragmentation, agricultural intensification and bushmeat

241 consumption. The role of bats as epidemiological reservoir of infectious agents needs to be

242 balanced with such human driven modifications on ecosystem functioning, in order to properly

243 assess bat-borne CoV emergence risks.

244

245 Materials and methods

246 Origin of the tested samples

247 Samples obtained from vouchered bat specimens during previous studies in Mozam-

248 bique (February and May 2015), Mayotte (November to December 2014), Reunion Island (Feb-

249 ruary 2015), Seychelles (February to March 2014), Mauritius (November 2012) and Madagas-

250 car (October to November 2014) were tested 54–57 (Supplementary Information). We also col-

251 lected additional swab samples from several synanthropic bat species on Madagascar, in Janu-

252 ary 2018 (Supplementary Information). Details on sample types, bat families, species, and lo-

253 cations are provided in Supplementary Table S3.

254

255 Ethical statement

256 The ethical terms of these research protocols were approved by the CYROI Institu-

257 tional Animal Care and Use Committee (Comité d’Ethique du CYROI no.114, IACUC certi-

258 fied by the French Ministry of Higher Education, of Research and Innovation). All protocols

259 strictly followed the terms of research permits and regulations for the handling of wild mam-

260 mals and were approved by licencing authorities (Supplementary Information).

261

262 Molecular detection

263 RNA was extracted from 140 μL of each sample using the QIAamp Viral RNA mini kit

264 (QIAGEN, Valencia, California, USA), and eluted in 60 μL of Qiagen AVE elution buffer. For

265 bat organs, approximately 1 mm3 of tissue (either lungs or intestines) was placed in 750 µL of

266 DMEM medium and homogenized in a TissueLyser II (Qiagen, Hilden, Germany) for 2 min at

267 25 Hz using 3 mm tungsten beads, prior to the RNA extraction. Reverse transcription was per-

268 formed on 10 μL of RNA using the ProtoScript II Reverse Transcriptase and Random Primer 6

269 (New England BioLabs, Ipswich, MA, USA) under the following thermal conditions: 70 °C for

270 5 min, 25 °C for 10 min, 42 °C for 50 min, and 65 °C for 20 min 58. cDNAs were tested for the

271 presence of the RNA-dependent RNA-polymerase (RdRp) gene using a multi-probe Real-Time

272 PCR 59. The primer set with Locked Nucleic Acids (LNA; underlined position in probe se-

273 quences) was purchased from Eurogentec (Seraing, Belgium): 11-FW: 5’-TGA-TGA-TGS-

274 NGT-TGT-NTG-YTA-YAA-3’ and 13-RV: 5’-GCA-TWG-TRT-GYT-GNG-ARC-ARA-

275 ATT-C-3’. Three probes were used: probe I (ROX): 5’-TTG-TAT-TAT-CAG-AAT-GGY-

276 GTS-TTY-AT-3’, probe II (FAM): 5’-TGT-GTT-CAT-GTC-WGA-RGC-WAA-ATG-TT-3’,

277 and probe III (HEX): 5’-TCT-AAR-TGT-TGG-GTD-GA-3’. Real-Time PCR was performed

278 with ABsolute Blue QPCR Mix low ROX 1X (Thermo Fisher Scientific, Waltham, MA, USA)

279 and 2.5 µL of cDNA under the following thermal conditions: 95 °C for 15 min, 95 °C for 30 s,

280 touchdowns from 56 °C to 50°C for 1 min and 50 cycles with 95 °C for 30 s and 50 °C for 1

281 min in a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA).

282 Because of the limited size of sequences generated from the Real-Time PCR, a second

283 PCR targeting 440 bp of the RdRp gene was performed with 5 µL of cDNA of each positive

284 sample, with the following primer set: IN-6: 5’-GGT-TGG-GAC-TAT-CCT-AAG-TGT-GA-

285 3’ and IN-7: 5’-CCA-TCA-TCA-GAT-AGA-ATC-ATC-ATA-3’ 60. PCRs were performed

286 with the GoTaq G2 Hot Start Green Master Mix (Promega, Madison, WI, USA) in an Applied

287 Biosystems 2720 Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA), under the

288 following thermal conditions: 95 °C for 2 min, 45 cycles with 95 °C for 1 min, 54 °C for 1 min,

289 72°C for 1 min, and a final elongation step at 72°C for 10 min. After electrophoresis in a 1.5%

290 agarose gel stained with 2% GelRed (Biotium, Hayward, CA, USA), amplicons of the expected

291 size were sequenced on both strands by Genoscreen (Lille, France). All generated sequences

292 were deposited in GenBank under the accession numbers MN183146 to MN183273.

293

294 Statistical analysis

295 We have performed Pearson χ² tests on all samples (1,013 bats) to explore the effect of

296 (i) location, (ii) bat family, and (iii) bat species on the detection of coronavirus RNA. Two

297 sampling campaigns, at two different season, in the same location, were available for Mozam-

298 bique. We thus investigated the effect of the sampling season, between the wet (February) and

299 dry (May) season, on CoV detection in Mozambique in 2015 (264 bats). Analyses were con-

300 ducted with R v3.5.1 software 61.

301

302 Phylogenetic analyses

303 Sequences obtained with the second PCR system 60 were edited with the Chromas Lite

304 Software package version 2.6.4 62. We explored CoV diversity of the sequences with pairwise

305 identity values obtained from seqidentity function in R bio3d package v2.3-4 63 and identified

306 the most similar CoV RdRp sequences referenced in GenBank using BLASTN 2.2.29+. An

307 alignment was then generated using the 51 nucleotide sequences obtained in this study and 151

308 reference CoV sequences representing a large diversity of hosts and geographic origins (Eu-

309 rope, Asia, Oceania, America and Africa), with CLC Sequence viewer 8.0 Software (CLC Bio,

310 Aarhus, Denmark). A phylogenetic tree was obtained by maximum likelihood using MEGA

311 Software v10.0.4 64, with 1,000 bootstrap iterations, and with the best evolutionary model for

312 our dataset as selected by modelgenerator v0.85 65.

313 Host-virus associations were investigated using the phylogeny of Western Indian Ocean

314 bats and their associated CoVs. Bat phylogeny was generated from an alignment of 1,030 bp of

315 mitochondrial Cytochrome b (Cyt b) gene sequences (Supplementary Table S4), for each CoV

316 positive bat species. Finally, bat and pruned CoV phylogenies based on each 393 bp RdRp

317 unique sequence fragment were generated by Neighbor-Joining with 1,000 bootstrap iterations,

318 using CLC Sequence viewer 8.0 Software (CLC Bio, Aarhus, Denmark)66. Phylogenetic con-

319 gruence was tested to assess the significance of the coevolutionary signal between bat host

320 species and CoVs sequences, using ParaFit with 999 permutations in the ape package v5.0 in

321 R 3.5.1 67,68. Tanglegram representations of the co-phylogeny were visualized using the Jane

322 software v4.01 69.

323

324 Acknowledgments

325 We are very grateful to S. Muradrasoli for recommendations on the optimization of the multi- 326 probe PCR protocol as well as for providing PCR controls and to C. Cordonin for providing bat 327 Cyt b sequences. L. Biscornet, C. Dionisio, L. Domergue, M. Dietrich, T. Mbohoahy, T. 328 Nekena, J. Rakotoarivelo, M. Rakotomanga, C. F. Rakotondramanana, and A. Randrenjarison 329 are thanked for their assistance in the field. We also thank S. Bos and A. Hoarau for their help 330 in the laboratory, and K. Dellagi and H. Pascalis for the development and the management of 331 the ‘partenariat Mozambique-Réunion dans la recherche en santé: pour une approche intégrée 332 d’étude des maladies infectieuses à risque épidémique (MoZaR)’ research program.

333

334 Competing interests

335 The authors declare no competing interests.

336 Data availability

337 DNA sequences: Genbank accessions MN183146 to MN183273

338

339 Funding

340 Field research was funded by the ‘Pathogènes associés à la Faune Sauvage Océan Indien (FS- 341 OI)’, the ‘Leptospirose Ocean Indien (LeptOI)’, the ‘Paramyxovirus Océan Indien (Para- 342 myxOI)’, and the ‘Partenariat Mozambique-Réunion dans la recherche en santé : pour une ap- 343 proche intégrée d’étude des maladies infectieuses à risque épidémique (MoZaR)’ programs 344 (Fond Européen de Développement Régional, Programme Opérationnel de Coopération Terri- 345 toriale). Fieldwork on Mayotte was funded by the ‘Centre National de la Recherche Scien- 346 tifique’ (Projets Exploratoires Premier Soutien BATMAN). Molecular analyses were finan- 347 cially supported by tutorship institutions of the UMR PIMIT. LJ is a recipient of a ‘Région 348 Réunion, European Regional Development Funds (FEDER 2014-2020)’ PhD fellowship. BR’s 349 post-doctoral fellowship was supported by the ‘Run Emerge’ European Union’s Seventh

350 Framework Program (FP7/2007–2013; Grant agreement NO 263958), the ‘Fonds de Coopé- 351 ration Régionale, Prefecture de La Réunion’, and The Field Museum of Natural History, Chi- 352 cago, through the Ralph and Marian Falk Medical Research Trust. CL is supported by a ‘Chaire 353 mixte : Université de La Réunion – INSERM’. The funding agencies were not involved in the 354 study design, implementation or publishing of this study, and the research presented herein 355 represents the opinions of the authors but not necessarily the opinions of the funding agencies.

356

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516 Figures

517

518 Figure 1. Geographic distribution of the tested samples. N: number of bats sampled for each 519 location. The open-source GIS software, QGIS v.3.6.1, was used to generate the map. 520 http://qgis.osgeo.org (2019).

521

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524

bioRxiv preprint doi: https://doi.org/10.1101/742866; this version posted March 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made ● Bat_CoV_FMNH_229303_Mozambique_ Mo.condylurus _2015_MN183182 1● Bat_CoV_FMNH_229296_Mozambique_ Mo.condylurus _2015_MN183179 available ●under aCC-BY-NC-NDBat_CoV_FMNH_229266_Mozambique_ 4.0 International Mo.condylurus _2015_MN183180license. ● Bat_CoV_FMNH_229293_Mozambique_ Mo.condylurus _2015_MN183181 ● Hipposideridae 1 ● Chaerephon_bat_coronavirus_KY22_ Ch.sp _Kenya_2006_HQ728486 ● BtCoV_NCL_MCO1_ Mo.condylurus _South_Africa_2012_KF843853 ● Megadermatidae ● PREDICT_GVF_CM_ECO70102_ Mo.condylurus _Cameroon_2013_KX284982 1 ● Bat_CoV_MAY027_Mayotte_ Ch.pusillus _2014_MN183176 ● Miniopteridae ● Bat_CoV_MAY025_Mayotte_ Ch.pusillus _2014_MN183175 ● Bat_CoV_MAY051_Mayotte_ Ch.sp _2014_MN183174 ● Molossidae 1● Bat_CoV_MAY015_Mayotte_ Ch.pusillus _2014_MN183173 ● Bat_CoV_MAY033_Mayotte_ Ch.pusillus _2014_MN183178 ● Mormoopidae 1 ● Bat_CoV_MAY004_Mayotte_ Ch.pusillus _2014_MN183177 ● Nycteridae ● Bat_CoV_UADBA_33728_Madagascar_ Mo.jugularis _2013_MN183183 ● Bat_CoV_UADBA_33735_Madagascar_ Mo.jugularis _2013_MN183186 ● Phyllostomidae ● Bat_CoV_FMNH_222698_Madagascar_ Mo.jugularis _2013_MN183185 1● Bat_CoV_FMNH_222691_Madagascar_ Mo.jugularis _2013_MN183187 ● Pteropodidae ● Bat_CoV_UADBA_33733_Madagascar_ Mo.jugularis _2013_MN183184 0.9 ● Bat_CoV_RB369_Reunion_ Mo.francoismoutoui_2015_MN183188 ● Rhinolophidae ● Bat_coronavirus_POA_ Uknown _Brazil_2012_144_KC110778 ● Bat_coronavirus_2173_ Cy.planirostris _Brazil_2014_KU552078 ● Rhinonycteridae ● Bat_CoV_FMNH_228953_Mozambique_ Rh.rhodesiae _2015_MN183148 ● Vespertillionidae ● Bat_CoV_FMNH_228945_Mozambique_ Rh.rhodesiae _2015_MN183146 ● Bat_CoV_FMNH_228957_Mozambique_ Rh.rhodesiae _2015_MN183150 1● Bat_CoV_FMNH_228962_Mozambique_ Rh.rhodesiae _2015_MN183147 ● Bat_CoV_FMNH_228955_Mozambique_ Rh.rhodesiae _2015_MN183151 Human CoV ● Bat_CoV_FMNH_228956_Mozambique_ Rh.rhodesiae _2015_MN183149 1 ● Bat_CoV_FMNH_228951_Mozambique_ Rh.sp _2015_MN183152 1● Bat_CoV_FMNH_228939_Mozambique_ Rh.lobatus _2015_MN183154 1 1● Bat_CoV_FMNH_228952_Mozambique_ Rh.rhodesiae _2015_MN183153 ● Bat_CoV_FMNH_228940_Mozambique_ Rh.lobatus _2015_MN183155 ● Bat_CoV_FMNH_228941_Mozambique_ Rh.lobatus _2015_MN183156 ● Kenya bat_coronavirus_BtKY83_ Rh.sp _Kenya_2007_GU065427 ● Bat_coronavirus_KY43_ Ca.cor _Kenya_2006_HQ728480 ● Bat_coronavirus_BB98−15_ Rh.blasii _Bulgaria_2008_GU190232 1● Bat_COV_HKU10_TLC1347A_ Hi.pomona _China_2010_JQ989273 ● Bat_COV_HKU10_175A_ Ro.leschenaultii _China_2005_JQ989271 0.9 ● Bat_coronavirus_2231_ Hi.diadema _Philippines_2010_AB683971 1● BtRf_AlphaCoV_HuB2013_ Rh.ferrumequinum _China_2013_KJ473807 ● BtMs_AlphaCoV_GS2013_ My.sp _China_2013_KJ473810 ● BtCoV_4307_1_ Rh.affinis _China_2013_KP876527 0.9● Bat_coronavirus_Aju21_ Ar.jamaicensis _Costa_Rica_2012_KC779226 1● BtCoV_KP565_ Ar.jamaicensis _Panama_2010_JQ731786 ● BtCoV_KP256_ Ar.jamaicensis _Panama_2010_JQ731784 0.9● Bat_CoV_FMNH_228980_Mozambique_ Mi.mossambicus _2015_MN183160 0.9● Bat_CoV_FMNH_228949_Mozambique_ Rh.rhodesiae _2015_MN183159 0.9● Bat_CoV_FMNH_228978_Mozambique_ Mi.mossambicus _2015_MN183157 1● Bat_CoV_FMNH_228969_Mozambique_ Mi.mossambicus _2015_MN183158 ● Miniopterus bat_coronavirus_KY27_ Mi.natalensis _Kenya_2006_HQ728484 1 ● Bat_CoV_FMNH_228971_Mozambique_ Mi.mossambicus _2015_MN183161 1● BtMf_AlphaCoV_AH2011_ Mi.fuliginosus _China_2011_KJ473795 0.8● Bat_coronavirus_1A_ Mi.magnater _China_2005_DQ666337 1 ● BtMf_AlphaCoV_HuB2013_ Mi.fuliginosus _China_2013_KJ473803 ● Miniopterus bat_coronavirus_1A_ Mi.sp _HongKong_2008_EU420138 ● Bat_coronavirus_1B_ Mi.sp _HongKong_2008_EU420137 0.9 1● Bat_coronavirus_Anlong_171_ Mi.schreibersii _China_2012_KF294275 ● Bat_coronavirus_HKU7_ Mi.magnater _DQ249226 1● Bat_coronavirus_CoV100_ Rh.megaphyllus _Australia_2007_EU834953 ● Bat_coronavirus_CoV132_ Mi.australis _Australia_2007_EU834954 ● Bat_coronavirus_HKU8_ Mi.pusillus _China_2004_DQ249228 ● Alphacoronavirus_BtCoV_MSTM2_ Mi.natalensis _South_Africa_2010_KF843851 1 ● BNM98_30_ Ny.leisleri _Bulgaria_2008_GU190239 ● Alphacoronavirus_ Ny.lasiopterus _Spain_2007_HQ184055 ● Pip1_Cr_ Pi.pipistrellus _France_2014_KT345294 1 ● BtCoV_VH_NC2_ Ne.capensis _South_Africa_2012_KF843854 ● BtNv_AlphaCoV_SC2013_ Ny.velutinus _China_2013_KJ473809 alpha ● Alphacoronavirus_Iprima_ Pi.kuhlii _Spain_2007_HQ184058 1 ● Bat_coronavirus_VM105_ My.dasycneme _Netherlands_2006_GQ259968 1 ● Bat_coronavirus_D5.70_ Pi.pygmaeus _Germany_2007_EU375867 ● Bat_coronavirus_Moxy4235_IT_16_ My.oxygnathus _Italy_2016_KY780395 ● Bat_coronavirus_HKU6_ My.pilosus _China_2004_DQ249224 ● CoV_75_55_L01_R1_CoV_BAT_VN_ Sc.kuhlii _Vietnam_KX092163 1● Bat_coronavirus_ My.lucifugus _Canada_2010_KY799179 ● Rocky_Mountain_Bat_Coronavirus_11_ My.lucifugus _USA_2011_EF544563 1 ● Bat_coronavirus_P1ab_ Mu.leucogaster _China_2013_KU182966 ● Lushi_Ml_bat_CoV_Neixiang_23_ Mu.leucogaster _China_2012_KF294375 ● PEDV_Porcine_Epidemic_COV_ Su.domesticus _1993_NC003436 1● BtCoV_LUX15_A_46_ My.emarginatus _Luxembourg_2015_KY502383 1 ● BtCoV_LUX15_A_59_ My.emarginatus _Luxembourg_2015_KY502386 1 ● Alphacoronavirus_13rs384_31_ My.blythii _Italy_2012_KF312400 1● Bat_coronavirus_Anlong_46_ Rh.pusillus _China_2013_KY770855 ● Bat_coronavirus_Anlong_60__ Ia.io _China_2013_KY770857 1 ● BtCoV_BRA182_ Mo.rufus _Brazil_2009_JQ731799 ● BtCoV_BRAP103_ Mo.currentium _Brazil_2009_JQ731800 ● Bat_CoV_FMNH_228887_Mozambique_ Tr.afer _2015_MN183162 ● NL63_related_bat_coronavirus_BtKYNL63_15_ Tr.afer _Kenya_2008_KY073746 ● Bat_CoV_FMNH_228891_Mozambique_ Tr.afer _2015_MN183163 ● Bat_CoV_FMNH_229232_Mozambique_ Tr.afer _2015_MN183165 ● Bat_CoV_FMNH_229212_Mozambique_ Tr.afer _2015_MN183164 1● Bat_CoV_FMNH_229241_Mozambique_ Tr.afer _2015_MN183166 ● Bat_CoV_FMNH_228892_Mozambique_ Tr.afer _2015_MN183167 0.7 ● Bat_CoV_FMNH_228888_Mozambique_ Tr.afer _2015_MN183168 1 Human_coronavirus_NL63_isolate_HCOV_005_ Ho.sapiens sapiens _ Kenya_2009_KP112154 Human_Cov_NL63_ Ho.sapiens sapiens_Netherlands_2004_NC005831 1● NL63_related_bat_coronavirus_BtKYNL63_9a_ Tr.afer _Kenya_2010_KY073744 ● Bat_CoV_FMNH_229243_Mozambique_ Tr.afer _2015_MN183169 0.8 ● Bat_CoV_FMNH_228933_Mozambique_ Hi.caffer _2015_MN183172 1● Bat_CoV_FMNH_228926_Mozambique_ Hi.caffer _2015_MN183171 ● Bat_CoV_FMNH_228981_Mozambique_ Hi.caffer _2015_MN183170 1 Alpaca_respiratory_coronavirus_isolate_CA08_1_ Vi.pacos _USA_2008_JQ410000 0.7 Camel229E_CoV_AC04_ Ca.dromedarius _SouthKorea_2014_KT253327 1● 229E_related_bat_CoV_BtCov_KY229E_8_ Hi.vittatus _Kenya_2010_KY073748 1 ● 229E_related_bat_CoV_BtCoV_AT1A_F45_ Hi.abae _Ghana_2010_KT253259 Human_coronavirus_229E_ Ho.sapiens sapiens_NC002645 ● 229E_related_bat_CoV_BtCoV_KW1C_F161_ Hi.ruber _Ghana_2010_KT253264 1 ● Bat_coronavirus_4292_ De.rotundus _Brazil_2013_KU552072 0.7 1 ● BtCoV_KP816_ Ph.discolor _Panama_2011_JQ731782 1 ● Bat_coronavirus_strain_328_11_ Mo.molossus _Brazil_2011_KX094982 ● Bat_coronavirus_1CO7BA_ Gl.soricina _Trinidad_2007_EU769558 0.8 ● BtCoV_KP534_ Ar.jamaicensis _Panama_2010_JQ731787 1 ● BtCoV_KCR253_ Ca.perspicillata _Costa_Rica_2010_JQ731789 1 ● Bat_cov_309_ Ca.castanea _Costa_Rica_2014_KM215147 1 ● Bat_coronavirus_1599_ Ca.perspicillata _Brazil_2012_KT717385 ● BtCoV_BRA118_ Ca.perspicillata _Brazil_2009_JQ731796 0.8 Porcine_enteric_alphacoronavirus_strain_GDS04_ Su.domesticus _China_2017_MF167434 1● SADS_related_coronavirus_ Rh.sp _China_MF094687 1 ● Bat_coronavirus_HKU2−1_ Rh.sp _China_2004_DQ249235 1 ● Bat_coronavirus_HKU2_GD−430_ Rh.sinicus _China_2004_NC009988 1● BtCoV_4307−2_ Rh.affinis _China_2013_KP876528 1 ● Bat_coronavirus_MLHJC4_ Rh.sinicus _China_2012_KU182954 ● BtRf_AlphaCoV_YN2012_ Rh.ferrumequinum _China_2012_KJ473808 1● Bat_CoV_FMNH_228963_Mozambique_ Rh.rhodesiae _2015_MN183190 ● Bat_CoV_FMNH_228954_Mozambique_ Rh.lobatus _2015_MN183189 0.9 Porcine_respiratory_coronavirus_ Su.domesticus _USA_2014_KR270796 1 Transmissible_gastroenteritis_virus_ Su.scrofa _China_2015_KX083668 1 Canine_coronavirus_strain_171_ Ca.lupus familiaris_Germany_1971_KC175339 0.9 Feline_coronavirus_UU15_ Fe.catus _Netherlands_2007_FJ938057 0.9 Mink_coronavirus_ Mu.vison _USA_1998_HM245925 ● Bat_coronavirus_1573_ St.lilium _Brazil_2012_KT717392 MERS_COV_Korea_Seoul_SNU1_035_Human_ Ho.sapiens sapiens_South_Korea_2015_KU308549 MERS_COV_Hu_Riyadh_KSA_12160_Human_ Ho.sapiens sapiens_South_Korea_2016_KX154688 MERS_COV_KFU_HKU_19Dam_ Ca.dromedarius _Saudi_Arabia_2013_KJ650296 MERS_COV_2c_EMC_Human_ Ho.sapiens sapiens_2012_JX869059 1 MERS_COV_D2731.3_14_ Ca.dromedarius _United_Arab_Emirates_KT751244 0.9 MERS_COV_ Ca.dromedarius _Egypt_2013_KJ477102 1 ● MERS_COV_related_5038_ Ne.capensis _South_Africa_2015_MF593268 1 1● BtCoV_UKR_G17_ Pi.nathusii _Ukraine_2011_KC243392 ● BtCoV_8_724_ Pi.pygmaeus _Romania_2009_KC243390 1 ● Bat_coronavirus_16BF104_ Ep.serotinus _South_Korea_2016_KY432468 0.9 ● Bat_Coronavirus_HKU5_ Pi.abramus _China_2007_NC009020 beta C CoV_2012_174_ Er.europaeus _Germany_2012_NC039207 ● Bat_Coronavirus_HKU4_ Ty.pachypus _NC009019 1 1● Bat_CoV_FMNH_228900_Mozambique_ Ny.thebaica _2015_MN183193 0.7 1● Bat_CoV_FMNH_228903_Mozambique_ Ny.thebaica _2015_MN183195 ● Bat_CoV_FMNH_228899_Mozambique_ Ny.thebaica _2015_MN183194 1 ● Bat_CoV_FMNH_228901_Mozambique_ Ny.thebaica _2015_MN183196 ● BtCoV_KW2E_F82_ Ny.gambiensis _Ghana_2011_JX899382 1● BtCoV_KW2E_F93_ Ny.gambiensis _Ghana_2010_JX899383 ● BtCoV_KW2E_F53_ Ny.gambiensis _Ghana_2011_JX899384 0.7● BtCoV_KCR230_ Pt.parnellii _CostaRica_2010_JQ731779 1● BtCoV_KCR180_ Pt.parnellii _CostaRica_2012_KC633193 1 ● BtCoV_KCR15_ Pt.parnellii _Costa_Rica_2012_KC633195 ● BtCoV_KCR155_ Pt.parnellii _CostaRica_2012_KC633194 ● Bat_coronavirus_49_ Pt.davyi _Mexico_2012_KC886322 SARS_COV_HKU39849_Human_ Ho.sapiens sapiens_United_Kingdom_2003_JN854286 1 SARS_coronavirus_SZ3_Civet_ Pa.larvata HongKong_2003_AY304486 ● SARS_coronavirus_Tor2_Human_ Ho.sapiens sapiens_Canada_2003_NC004718 1 ● Bat_SARS_CoV_Rf1_ Rh.ferrumequium _China_2004_DQ412042 0.9 ● Bat_SARS_COV_HKU3_ Rh.sinicus _China_2006_DQ022305 ● SARS_like_CoV_SL_CoVZXC21_ Rh.sinicus _China_2015_MG772934 0.8 ● Bat_SARS_coronavirus_Rm1_ Rh.macrotis _China_2004_DQ412043 ● SARS_bat_coronavirus_Is39_ Rh.sp _Japan_2013_AB889995 beta B 1 1 ● SARS_related_BtCoV_ Rh.ferrumequinum _Luxembourg_2016_KY502395 ● Bat_coronavirus_BB98−16_ Rh.blasii _Bulgaria_2008_GU190217 ● Betacoronavirus_187632_2_ Rh.hipposideros _Italy_2012_KF500952 1 ● Bat_coronavirus_292_ Hi.caffer _Gabon_2009_JX174638 1 ● Zaria_bat_coronavirus_ Hi.commersoni _Nigeria_2008_HQ166910 ● Bat_Hp_betacoronavirus_Zhejiang2013_ Hi.pratti _2013_China_KF636752 11 Sable_Antelope_Cov_ Hi.niger _USA_2003_EF424621 1 Bovine_COV_ Bo.sp _NC_003045 0.8 Human_coronavirus_OC43_ Ho.sapiens sapiens_AY391777 Rabbit_COV_HKU14_ Or.cuniculus _China_2006_NC_017083 beta A 1 MHV_COV_StrainA59_ Mu.musculus _USA_2014_MF618253 Human_coronavirus_HKU1_N11_genotype_A_ Ho.sapiens _DQ415908 1 ● Bat_coronavirus_GLCC1_ Cy.sphinx _China_2005_KU182962 0.9 ● Bat_coronavirus_Diliman1525G2_ Cy.brachyotis _Philippines_2008_AB539082 0.9 ● Bat_coronavirus_2265_ Pt.jagori _Philippines_2010_AB683971 1 ● Bat_coronavirus_KY06_ Ro.aegyptiacus _Kenya_2006_HQ728484 0.9 1 ● Bat_coronavirus_HKU9_ Ro.leschenaultii _China_NC009022 ● Bat_coronavirus_HKU9_PREDICT_LAP11_K0006_ Eo.spelaea _Laos_2011_KX284911 ● Bat_coronavirus_NWDC188_ Me.kusnotoi _China_2013_KU182986 1● PREDICT_CoV_22_LAP11_D0063_ Eo.spelaea _Laos_2011_KX284906 1 ● Bat_coronavirus_RK074_ Eo.spelea _China_2010_KX520659 0.7● BtKY89_ Ei.helvum _Kenya_2007_GU065433 beta D 1● Bat_coronavirus_KY24_ Ei.helvum _Kenya_2006_HQ728482 1● Bat_CoV_UADBA_50636_Madagascar_ Ro.madagascariensis _2014_MN183192 1 ● Bat_CoV_UADBA_50625_Madagascar_ Ro.madagascariensis _2014_MN183191 ● PREDICT_CD116088_ Ep.franqueto _Congo_2014_KX285095 1 ● PREDICT_CD116004_ Mi.pusillus _Congo_2014_KX285087 ● PREDICT_GVF_CM_ECO70527_ Ep.gambianus _Cameroon_2013_KX285008 ● PREDICT_AATHA_ Ep.sp _Tanzania_2013_KX285299 White_eye_COV_HKU16_ Zo.sp _China_2007_NC016991 1 Coronavirus_HKU15_ Su.domesticus _China_2014_LC216914 0.8 Bulbul_coronavirus_HKU11_934_ Py.jocosus _China_2007_FJ376619 Munia_COV_HKU13_3514_ Lo.striata _China_2007_NC011550 delta 1 Wigeon_COV_HKU20_ Ma.penelope _China_2008_NC016995 Night_Heron_COV_HKU19_ Ny.nycticorax _China_2007_NC_016994 0.7 1 Infectious_bronchitis_virus_isolate_YN_ Ga.gallusdomesticus _China_2005_JF893452 1 Duck_coronavirus_isolate_DK_CH_HN_ZZ2004_ An.platyrhynchos _China_2004_JF705860 0.7 Turkey_coronavirus_strain_TCoV_VA_74_03_ Me.gallopavo _GQ427173 1 Beluga_Whale_coronavirus_SW1_ De.leucas _NC010646 gamma Bottlenose_dolphin_coronavirus_HKU22_isolate_CF090331_ Tu.sp _KF793826 0.1 bioRxiv preprint doi: https://doi.org/10.1101/742866; this version posted March 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

526 Figure 3. Maximum Likelihood (ML) consensus tree derived from 202 coronavirus (CoV) 527 RNA-dependent RNA-polymerase partial nucleotide sequences (393 bp). Colored circles at the 528 end of branches indicate bat family origin. Sequences in bold refer to bat CoVs detected in this 529 study. Bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean number of nu- 530 cleotide substitutions per site. The tree was generated with the General Time Reversible evolu- 531 tionary model (GTR+I+Г, I = 0.18, α = 0.64) and 1,000 bootstrap replicates.

532

533 Figure 4. Detail of the α-CoV clade. Molossidae CoVs generated in the study are indicated in 534 bold. This sub-tree is a zoom on Molossidae CoV clade from the tree depicted in Figure 3. Boot- 535 strap values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide sub- 536 stitutions per site.

537

538 Figure 5. Detail of the α-CoV clade. NL63-like CoVs generated in the study are indicated in 539 bold. This sub-tree is a zoom on NL63 CoV clade from the tree depicted in Figure 3. Only 540 bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide 541 substitutions per site.

542 Figure 6. Detail of the α-CoV clade. 229E-like CoVs generated in the study are indicated in 543 bold. This sub-tree is a zoom on NL63 CoV clade from the tree depicted in Figure 3. Bootstrap 544 values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide substitu- 545 tions per site.

546

547 Figure 7. Detail of the β-C CoV clade. CoVs generated in the study are indicated in bold. This 548 sub-tree is a zoom on β-C CoV clade from the tree depicted in Figure 3. Bootstrap values >0.7 549 are indicated on the tree. Scale bar indicates mean number of nucleotide substitutions per site.

550

551 Figure 8. Detail of the β-D CoV. CoVs generated in the study are indicated in bold. This sub- 552 tree is a zoom on β-D CoV clade from the tree depicted in Figure 3. Bootstrap values >0.7 are 553 indicated on the tree. Scale bar indicates mean number of nucleotide substitutions per site.

554

555

556 Figure 9. Tanglegram representing host-virus co-evolution between bats of the Western Indian 557 Ocean and their associated CoVs. Phylogeny of bats (left) was constructed with an alignment 558 of 11 Cyt b sequences of 1,030 bp by Neighbor-Joining with 1,000 bootstrap iterations. Pruned 559 phylogeny of Western Indian Ocean bats CoVs (right) was constructed with an alignment of 27 560 unique sequences of 393 bp from Western Indian Ocean bats CoVs, by Neighbor-Joining with 561 1,000 bootstrap iterations.